Nasser M. Al-Hajri

A numerical method to predict crossflow rate resulted from downhole leaks

Water injection wells often develop casing leaks that result in crossflow between two formations. The crossflow can be upward or downward depending on the respective location of each formation and the gravitation hydrostatic column between the formations. Traditionally, crossflow rates are measured by running production logs (flowmeter); a process that require well intervention. Such intervention in a well with compromised downhole integrity is not preferred as it incur additional cost and exposes the oilfield operator to possible mechanical risks such as stuck tools. In this work, we present a numerical method to predict crossflow rates without such intervention. The method requires readily available input from the water injection that includes injection flow rates and pressures, in addition, to shut-in well data. The workflow developed in this study includes nodal analysis and well performance modeling. A water injection well (Well-XYZ) was selected to validate the developed workflow as a case study in this paper. Well-XYZ has an upward crossflow that resulted from a downhole leak. The numerical method starts with building a well performance model before the leak develops. The injection data (pressure and flow rates) before leak development are used for this model. Second, a well with the same physical dimensions of Well-XYZ is imagined and modeled as a producing well from the downhole formation to the leak point. The shut-in well data of Well-XYZ are used to build the production well model. Third, we choose the leak point as the system node and obtain its pressure either by direct measurement or through calculation. Once the system node pressure is obtained, it is inputted as the wellhead producing pressure of the imagined well and its production rate will be the crossflow rate. The results of this method were tested by running a production log on Well-XYZ. The results of the production log showed that the method was able to accurately predict the crossflow rate with a less than 2% error.